The present invention is generally directed to email, and more particularly to identifying critical emails.
Emails are becoming a preferred communication channel for customer service. For customers, it is a way to avoid long hold times on voice channels and to document the exchange. For companies, it offers an opportunity to best utilize customer service representatives by evenly distributing the workload over time, and, for representatives, it allows time to research the issue and respond to the customers in a manner consistent with company policies. Companies can further exploit the offline nature of emails by automatically routing the emails involving special issues to representatives trained in dealing with such issues.
Besides specialized product and service related issues, every business must ensure emails containing an emotional component, such as language expressing anger, sadness, disappointment, etc., are handled with care. Such emails are critical for businesses. Their careful handling helps customer retention, i.e., retaining customers who otherwise would have taken their business elsewhere. Furthermore, critical emails provide valuable feedback for business process improvement.
To detect critical emails, a critical email detector is typically constructed. An approach to building a critical email detector is to train a statistical classifier to generate a model. The statistical classifier can then use the model to analyze the text of emails and classify each email as critical or not critical. For such an approach, a significant amount of expensive training data is typically needed to generate a reliable model. To create training data, a large number of customer emails are analyzed by human labelers and labeled as critical or not. Furthermore, to train an accurate classifier, however, it is important to have as little noise in the labeling of training data as possible. This requires a very high level of agreement among human labelers. Given the subjective nature of the task, however, a high level of agreement among labelers is often difficult to obtain. Some labelers may consider an email to be critical while others may not.
As a result, there remains a need for a more accurate way to identify critical emails.